An improved iterative minimum mean-squared error (MMSE) channel estimation method is proposed for joint coding and precoding OFDM systems. Compared with the traditional simplified estimator, the proposed scheme provides higher estimation quality with slight complexity increment at low signal-to-noise ratio (SNR) values. The performance of the iterative receiver including the proposed estimator approaches that of the perfect MMSE estimator without any simplification.

Ultra-wideband (UWB) beamforming is now attracting significant research attention for attaining spatial gain from array antennas. It is commonly believed that directional antenna based communication could improve the system performance. In order to further make clear the relationship between system performance and the antenna array beamforming, UWB indoor channels are extracted from practical measurements and circular horn antenna is used to characterize the channel properties and to evaluate the system performance. Using a single beam directional antenna with a certain half power beamwidth (HPBW), the channel capacity and the bit-error-rate (BER) performance of a UWB RAKE receiver are evaluated. In the line-of-sight (LOS) environments, the channel capacity and BER performance are improved with the beamwidth becoming smaller. However in the non-line-of-sight (NLOS) environments, the capacity and BER performance are not always better with directional antennas. And the variation trend between the system performance and the antenna beamwidth disappears. This is mainly because that there exist no dominant strong path components like those seen in LOS environments. Then beam combining is introduced to further improve the system performance. Simulation results show that the channel capacity and BER performance cloud be greatly improved by multiple beam combining, especially for the NLOS environments. This reminds us that when antenna beamforming is used to obtain array gain, the beamwidth should be carefully designed and beam combining is necessary to obtain optimal performance, especially in NLOS environments.

An improved soft-input soft-output (SISO) minimum mean-squared error (MMSE) detection method is proposed for joint coding and precoding OFDM systems under imperfect channel estimation. Compared with the traditional mismatched detection which uses the channel estimate as its exact value, the signal model of the proposed detector is more accurate and the influence of channel estimation error (CEE) can be effectively mitigated. Simulations indicate that the proposed scheme can improve the bit error rate (BER) performance with fewer pilot symbols.

In this letter, a simple dispersion matrix design method for generalized space-time shift keying is presented, in which the dispersion matrices are systematically constructed with cyclic identity matrix, without the need of computer search. The proposed scheme is suitable for any number of transmit antennas greater than two, and can achieve the transmit diversity order of two except two special cases. Simulation results are presented to verify our theoretical analysis and the performance of the proposed scheme.

A novel virtual sensors-based positioning method has been presented in this paper, which can make use of both direct paths and indirect paths. By integrating the virtual sensor idea and Bayesian state and observation framework, this method models the indirect paths corresponding to persistent virtual sensors as virtual direct paths and further reformulates the wireless positioning problem as the maximum likelihood estimation of both the mobile terminal's positions and the persistent virtual sensors' positions. Then the method adopts the EM (Expectation Maximization) and the particle filtering schemes to estimate the virtual sensors' positions and finally exploits not only the direct paths' measurements but also the indirect paths' measurements to realize the mobile terminal's positions estimation, thus achieving better positioning performance. Simulation results demonstrate the effectiveness of the proposed method.

In this letter, a novel channel impulse response (CIR)-based fingerprinting positioning method using kernel principal component analysis (KPCA) has been proposed. During the offline phase of the proposed method, a survey is performed to collect all CIRs from access points, and a fingerprint database is constructed, which has vectors including CIR and physical location. During the online phase, KPCA is first employed to solve the nonlinearity and complexity in the CIR-position dependencies and extract the principal nonlinear features in CIRs, and support vector regression is then used to adaptively learn the regress function between the KPCA components and physical locations. In addition, the iterative narrowing-scope step is further used to refine the estimation. The performance comparison shows that the proposed method outperforms the traditional received signal strength based positioning methods.